Hybrid Computational Intelligence for Pattern Analysis and Understanding
Aim & scope
The Hybrid Computational Intelligence for Pattern Analysis series aims to provide insights into the latest trends in hybrid intelligent algorithms and architectures. It also focuses on the application perspectives of these hybrids intelligent techniques to real-world pattern analysis and understanding. The series aims to assist researchers who are focused on the careful analysis of large volumes of image/pattern specific data, in order to extract meaningful information through different hybrid intelligent algorithms. Individual volumes are self-contained and supplemented by case studies, source codes, and video demonstrations to enable proper understanding of the target audience.
Key features
Provides insights into the latest trends in hybrid intelligent algorithms and architectures
Focuses on the application of hybrid intelligent techniques for pattern recognition and analysis
Volumes include source codes, cases studies, data sets and video demonstrations
Written for researchers and students who want to understand the application of hybrid computational intelligence advances and explore the significance of hybrid computational intelligence for pattern analysis
Topical coverage includes but is not limited to:
Hybrid Computational Intelligence [Neuro-Fuzzy, Rough-neuro, Rough-fuzzy, Fuzz-Rough, Fuzz-Evolutionary, Neuro-Evolutionary, Neuro-Fuzz-Evolutionary, Quantum-Fuzzy, Quantum-Neuro, Quantum-Evolutionary] algorithms and architectures, and their application to a wide range of pattern analysis:
Image and pattern mining Pattern recognition and analysis Biomedical text mining Voice and speech recognition and analysis Chemometrics Deep metric learning for pattern recognition Multimodal pattern recognition of social signals in HCI Special hardware architectures for pattern recognition Logical combinatorial pattern recognition Gesture analysis for human-robot interaction Human mind analysis Real-time video processing and analysis Stereo-to-auto stereoscopic 3D video conversion Virtual and augmented reality Multi-modal image registration Content-Based Image Retrieval (CBIR) Interventional image analysis Pattern recognition in remote sensing Statistical techniques in pattern recognition Graph-based representations
New volume proposals
Volumes can be Edited, Multi-Authored, or Authored Monographs
New volume proposals should:
Include a well-structured Table of Contents
Be innovative, including original features, and any overlaps with published titles in the HCIPAU Series should be explained
Include a list of confirmed or tentative, geographically distributed, authors (for Edited volumes)
Indexing
All published volumes in this book series are submitted for indexing in:
Scopus
EI Indexing / Compendex
Book Citation Index
Google Scholar
Audience
Graduate students, researchers, and professionals interested in developing the application of hybrid computational intelligence advances and exploring the significance of hybrid computational intelligence for pattern analysis
Publish a book with Elsevier
Series Editors
SB
Siddhartha Bhattacharyya
Senior Researcher
VSB Technical University of Ostrava, Czech Republic
繼續閱讀 Siddhartha Bhattacharyya
ND
Nilanjan Dey
Associate Professor
Department of Computer Science and Engineering, Techno International New Town, Kolkata, India
繼續閱讀 Nilanjan Dey